Bayesian Mixed-effects Models

Bayesian Mixed-Effects Models is a statistical modeling framework that extends the class of mixed-effects models by incorporating Bayesian inference to estimate fixed and random effects, distinguishing itself through the use of prior distributions and posterior updating to account for uncertainty in model parameters.

319

Publications

36.3K

Citations

1.1K

Authors

605

Institutions

Publications per year

2017–2026

123

Variants

Bayesian Mixed Effects Models

Authors

1.1K

  1. YH
    Yangxin Huang

    University of South Florida, United States

    10 publications · 280 citations

    10

    h-index

  2. TY
    Tal Yarkoni

    The University of Texas at Austin, United States

    6 publications · 296 citations

    6

    h-index

  3. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    6 publications · 550 citations

    6

    h-index

  4. GD
    Getachew Dagne

    University of South Florida, United States

    6 publications · 187 citations

    6

    h-index

  5. JM
    Julia M. Haaf

    University of California, Irvine, United States

    5 publications · 210 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Statistics in Medicine

    23 publications · 1K citations

    16

    h-index

  2. Journal of the American Statistical Association

    13 publications · 4.6K citations

    13

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 13 publications · 676 citations

    12

    h-index

  4. Biometrics

    9 publications · 632 citations

    9

    h-index

  5. Journal of Statistical Planning and Inference

    7 publications · 239 citations

    7

    h-index

1–5